def main(args):
args_dict = vars(args)
print('args: {}'.format(args_dict))
with tf.Graph().as_default() as g:
# rollout subgraph
with tf.name_scope('rollout'):
observations = tf.placeholder(shape=(None, OBSERVATION_DIM), dtype=tf.float32)
logits = build_graph(observations)
logits_for_sampling = tf.reshape(logits, shape=(1, len(ACTIONS)))
# Sample the action to be played during rollout.
sample_action = tf.squeeze(tf.multinomial(logits=logits_for_sampling, num_samples=1))
optimizer = tf.train.RMSPropOptimizer(
learning_rate=args.learning_rate,
decay=args.decay
)
# dataset subgraph for experience replay
with tf.name_scope('dataset'):
# the dataset reads from MEMORY
ds = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32, tf.float32))
ds = ds.shuffle(MEMORY_CAPACITY).repeat().batch(args.batch_size)
iterator = ds.make_one_shot_iterator()
# training subgraph
with tf.name_scope('train'):
# the train_op includes getting a batch of data from the dataset, so we do not need to use a feed_dict when running the train_op.
next_batch = iterator.get_next()
train_observations, labels, processed_rewards = next_batch
# This reuses the same weights in the rollout phase.
train_observations.set_shape((args.batch_size, OBSERVATION_DIM))
train_logits = build_graph(train_observations)
cross_entropies = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=train_logits,
labels=labels
)
# Extra loss when the paddle is moved, to encourage more natural moves.
probs = tf.nn.softmax(logits=train_logits)
move_cost = args.laziness * tf.reduce_sum(probs * [0, 1.0, 1.0], axis=1)
loss = tf.reduce_sum(processed_rewards * cross_entropies + move_cost)
global_step = tf.train.get_or_create_global_step()
train_op = optimizer.minimize(loss, global_step=global_step)
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=args.max_to_keep)
with tf.name_scope('summaries'):
rollout_reward = tf.placeholder(
shape=(),
dtype=tf.float32
)
# the weights to the hidden layer can be visualized
hidden_weights = tf.trainable_variables()[0]
for h in range(args.hidden_dim):
slice_ = tf.slice(hidden_weights, [0, h], [-1, 1])
image = tf.reshape(slice_, [1, 80, 80, 1])
tf.summary.image('hidden_{:04d}'.format(h), image)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
tf.summary.scalar('{}_max'.format(var.op.name), tf.reduce_max(var))
tf.summary.scalar('{}_min'.format(var.op.name), tf.reduce_min(var))
tf.summary.scalar('rollout_reward', rollout_reward)
tf.summary.scalar('loss', loss)
merged = tf.summary.merge_all()
print('Number of trainable variables: {}'.format(len(tf.trainable_variables())))
inner_env = gym.make('Pong-v0')
# tf.agents helper to more easily track consecutive pairs of frames
env = FrameHistory(inner_env, past_indices=[0, 1], flatten=False)
# tf.agents helper to automatically reset the environment
env = AutoReset(env)
with tf.Session(graph=g) as sess:
if args.restore:
restore_path = tf.train.latest_checkpoint(args.output_dir)
print('Restoring from {}'.format(restore_path))
saver.restore(sess, restore_path)
else:
sess.run(init)
summary_path = os.path.join(args.output_dir, 'summary')
summary_writer = tf.summary.FileWriter(summary_path, sess.graph)
# lowest possible score after an episode as the
# starting value of the running reward
_rollout_reward = -21.0
for i in range(args.n_epoch):
print('>>>>>>> epoch {}'.format(i+1))
print('>>> Rollout phase')
epoch_memory = []
episode_memory = []
# The loop for actions/stepss
_observation = np.zeros(OBSERVATION_DIM)
while True:
# sample one action with the given probability distribution
_label = sess.run(sample_action, feed_dict={observations: [_observation]})
_action = ACTIONS[_label]
_pair_state, _reward, _done, _ = env.step(_action)
if args.render:
env.render()
# record experience
episode_memory.append((_observation, _label, _reward))
# Get processed frame delta for the next step
pair_state = _pair_state
current_state, previous_state = pair_state
current_x = prepro(current_state)
previous_x = prepro(previous_state)
_observation = current_x - previous_x
if _done:
obs, lbl, rwd = zip(*episode_memory)
# processed rewards
prwd = discount_rewards(rwd, args.gamma)
prwd -= np.mean(prwd)
prwd /= np.std(prwd)
# store the processed experience to memory
epoch_memory.extend(zip(obs, lbl, prwd))
# calculate the running rollout reward
_rollout_reward = 0.9 * _rollout_reward + 0.1 * sum(rwd)
episode_memory = []
if args.render:
_ = input('episode done, press Enter to replay')
epoch_memory = []
continue
if len(epoch_memory) >= ROLLOUT_SIZE:
break
# add to the global memory
MEMORY.extend(epoch_memory)
print('>>> Train phase')
print('rollout reward: {}'.format(_rollout_reward))
# Here we train only once.
_, _global_step = sess.run([train_op, global_step])
if _global_step % args.save_checkpoint_steps == 0:
print('Writing summary')
feed_dict = {rollout_reward: _rollout_reward}
summary = sess.run(merged, feed_dict=feed_dict)
summary_writer.add_summary(summary, _global_step)
save_path = os.path.join(args.output_dir, 'model.ckpt')
save_path = saver.save(sess, save_path, global_step=_global_step)
print('Model checkpoint saved: {}'.format(save_path))
def main(args):
args_dict = vars(args)
print('args: {}'.format(args_dict))
with tf.Graph().as_default() as g:
# rollout subgraph
with tf.name_scope('rollout'):
observations = tf.placeholder(shape=(None, OBSERVATION_DIM), dtype=tf.float32)
logits = build_graph(observations)
logits_for_sampling = tf.reshape(logits, shape=(1, len(ACTIONS)))
# Sample the action to be played during rollout.
sample_action = tf.squeeze(tf.multinomial(logits=logits_for_sampling, num_samples=1))
optimizer = tf.train.RMSPropOptimizer(
learning_rate=args.learning_rate,
decay=args.decay
)
# dataset subgraph for experience replay
with tf.name_scope('dataset'):
# the dataset reads from MEMORY
ds = tf.data.Dataset.from_generator(gen, output_types=(tf.float32, tf.int32, tf.float32))
ds = ds.shuffle(MEMORY_CAPACITY).repeat().batch(args.batch_size)
iterator = ds.make_one_shot_iterator()
# training subgraph
with tf.name_scope('train'):
# the train_op includes getting a batch of data from the dataset, so we do not need to use a feed_dict when running the train_op.
next_batch = iterator.get_next()
train_observations, labels, processed_rewards = next_batch
# This reuses the same weights in the rollout phase.
train_observations.set_shape((args.batch_size, OBSERVATION_DIM))
train_logits = build_graph(train_observations)
cross_entropies = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=train_logits,
labels=labels
)
# Extra loss when the paddle is moved, to encourage more natural moves.
probs = tf.nn.softmax(logits=train_logits)
move_cost = args.laziness * tf.reduce_sum(probs * [0, 1.0, 1.0], axis=1)
loss = tf.reduce_sum(processed_rewards * cross_entropies + move_cost)
global_step = tf.train.get_or_create_global_step()
train_op = optimizer.minimize(loss, global_step=global_step)
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=args.max_to_keep)
with tf.name_scope('summaries'):
rollout_reward = tf.placeholder(
shape=(),
dtype=tf.float32
)
# the weights to the hidden layer can be visualized
hidden_weights = tf.trainable_variables()[0]
for h in range(args.hidden_dim):
slice_ = tf.slice(hidden_weights, [0, h], [-1, 1])
image = tf.reshape(slice_, [1, 80, 80, 1])
tf.summary.image('hidden_{:04d}'.format(h), image)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
tf.summary.scalar('{}_max'.format(var.op.name), tf.reduce_max(var))
tf.summary.scalar('{}_min'.format(var.op.name), tf.reduce_min(var))
tf.summary.scalar('rollout_reward', rollout_reward)
tf.summary.scalar('loss', loss)
merged = tf.summary.merge_all()
inner_env = gym.make('Pong-v0')
# tf.agents helper to more easily track consecutive pairs of frames
env = FrameHistory(inner_env, past_indices=[0, 1], flatten=False)
# tf.agents helper to automatically reset the environment
env = AutoReset(env)
with tf.Session(graph=g) as sess:
if args.restore:
restore_path = tf.train.latest_checkpoint(args.output_dir)
print('Restoring from {}'.format(restore_path))
saver.restore(sess, restore_path)
else:
sess.run(init)
summary_path = os.path.join(args.output_dir, 'summary')
summary_writer = tf.summary.FileWriter(summary_path, sess.graph)
# lowest possible score after an episode as the
# starting value of the running reward
_rollout_reward = -21.0
for i in range(args.n_epoch):
print('>>>>>>> epoch {}'.format(i+1))
print('>>> Rollout phase')
epoch_memory = []
episode_memory = []
# The loop for actions/stepss
_observation = np.zeros(OBSERVATION_DIM)
while True:
# sample one action with the given probability distribution
_label = sess.run(sample_action, feed_dict={observations: [_observation]})
_action = ACTIONS[_label]
_pair_state, _reward, _done, _ = env.step(_action)
if args.render:
env.render()
# record experience
episode_memory.append((_observation, _label, _reward))
# Get processed frame delta for the next step
pair_state = _pair_state
current_state, previous_state = pair_state
current_x = prepro(current_state)
previous_x = prepro(previous_state)
_observation = current_x - previous_x
if _done:
obs, lbl, rwd = zip(*episode_memory)
# processed rewards
prwd = discount_rewards(rwd, args.gamma)
prwd -= np.mean(prwd)
prwd /= np.std(prwd)
# store the processed experience to memory
epoch_memory.extend(zip(obs, lbl, prwd))
# calculate the running rollout reward
_rollout_reward = 0.9 * _rollout_reward + 0.1 * sum(rwd)
episode_memory = []
if args.render:
_ = input('episode done, press Enter to replay')
epoch_memory = []
continue
if len(epoch_memory) >= ROLLOUT_SIZE:
break
# add to the global memory
MEMORY.extend(epoch_memory)
print('>>> Train phase')
print('rollout reward: {}'.format(_rollout_reward))
# Here we train only once.
_, _global_step = sess.run([train_op, global_step])
if _global_step % args.save_checkpoint_steps == 0:
print('Writing summary')
feed_dict = {rollout_reward: _rollout_reward}
summary = sess.run(merged, feed_dict=feed_dict)
summary_writer.add_summary(summary, _global_step)
save_path = os.path.join(args.output_dir, 'model.ckpt')
save_path = saver.save(sess, save_path, global_step=_global_step)
print('Model checkpoint saved: {}'.format(save_path))